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Video Super Resolution On Vid4 4X Upscaling 1

Metrics

PSNR
SSIM

Results

Performance results of various models on this benchmark

Model Name
PSNR
SSIM
Paper TitleRepository
IconVSR28.040.8570BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond-
VRT29.420.8795VRT: A Video Restoration Transformer-
TOFlow25.850.7659Video Enhancement with Task-Oriented Flow-
BasicVSR27.960.8553BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond-
FRVSR26.690.8103Frame-Recurrent Video Super-Resolution-
RLSP27.480.8388Efficient Video Super-Resolution through Recurrent Latent Space Propagation-
FTVSR28.70.869Learning Spatiotemporal Frequency-Transformer for Low-Quality Video Super-Resolution-
PFNL27.160.8355Progressive Fusion Video Super-Resolution Network via Exploiting Non-Local Spatio-Temporal Correlations
DUF27.380.8329Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation-
TTVSR28.400.8643Learning Trajectory-Aware Transformer for Video Super-Resolution-
BasicVSR++29.040.8753BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment-
TGA27.630.8423Video Super-resolution with Temporal Group Attention-
RRN27.690.8488Revisiting Temporal Modeling for Video Super-resolution-
RSDN27.920.8505Video Super-Resolution with Recurrent Structure-Detail Network-
RBPN27.170.8205Recurrent Back-Projection Network for Video Super-Resolution-
EDVR27.850.8503EDVR: Video Restoration with Enhanced Deformable Convolutional Networks-
GOVSR28.410.8724Omniscient Video Super-Resolution-
RVRT29.540.8810Recurrent Video Restoration Transformer with Guided Deformable Attention-
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Video Super Resolution On Vid4 4X Upscaling 1 | SOTA | HyperAI